Data-Driven Design-by-Analogy: State of the Art and Future Directions

Journal of Mechanical Design ◽

10.1115/1.4051681 ◽

2021 ◽

pp. 1-55

Author(s):

Shuo Jiang ◽

Jie Hu ◽

Kristin L. Wood ◽

Jianxi Luo

Keyword(s):

Design Methodology ◽

Data Science ◽

State Of The Art ◽

Data Driven ◽

Target Domain ◽

Future Directions ◽

Structured Analysis ◽

Design By Analogy ◽

Conceptual Data ◽

Design Databases

Abstract Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

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Data-Driven Design-by-Analogy: State of the Art

10.1115/detc2021-68669 ◽

2021 ◽

Author(s):

Shuo Jiang ◽

Jie Hu ◽

Jianxi Luo

Keyword(s):

Design Methodology ◽

Data Science ◽

State Of The Art ◽

Data Driven ◽

Literature Analysis ◽

Target Domain ◽

Individual Study ◽

Source Domain ◽

Design By Analogy ◽

Design Databases

Abstract Design-by-Analogy (DbA) is a design methodology that draws inspiration from a source domain to a target domain to generate new solutions to problems or designs, which can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. Herein, we survey the prior data-driven DbA studies and categorize and analyze individual study according to the data, methods and applications in four categories including analogy encoding, retrieval, mapping, and evaluation. Based on such structured literature analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field.

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State-of-the-Art in Performance Metrics and Future Directions for Data Science Algorithms

Journal of scientific research ◽

10.37398/jsr.2020.640232 ◽

2020 ◽

Vol 64 (02) ◽

pp. 221-238

Author(s):

Ajay Sharma ◽

Promad Kumar Mishra

Keyword(s):

Data Science ◽

Performance Metrics ◽

State Of The Art ◽

Future Directions

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Data science for hospitality and tourism

Worldwide Hospitality and Tourism Themes ◽

10.1108/whatt-07-2018-0050 ◽

2018 ◽

Vol 10 (6) ◽

pp. 717-725

Author(s):

Paulo Rita ◽

Nicole Rita ◽

Cristina Oliveira

Keyword(s):

Design Methodology ◽

Data Science ◽

Scientific Literature ◽

State Of The Art ◽

Academic Research ◽

Future Research ◽

Literature Analysis ◽

Content Type ◽

Hospitality And Tourism ◽

Productive Authors

Purpose This paper aims to embrace the challenge of performing a state-of-the-art scientific literature analysis in data science for hospitality and tourism. This is important because relatively little contemporary analysis has been published. Design/methodology/approach Data on over 800 publications were collected from the Scopus database and analyzed by the differing types of publications, evolution of publications across time, top publishers and outlets, publications per area and per topic, top keywords used, most cited papers and most productive authors. Findings Conclusions are drawn and some suggestions are offered regarding topics that are likely to provide opportunities for future research. Originality/value This paper identifies the need for analysis on state-of-the-art academic research published to-date on the application of methods and techniques relating to data science in hospitality and tourism.

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Data-driven approaches in FinTech: a survey

Information Discovery and Delivery ◽

10.1108/idd-06-2020-0062 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xin Tian ◽

Jing Selena He ◽

Meng Han

Keyword(s):

Workforce Development ◽

Portfolio Management ◽

Data Privacy ◽

Design Methodology ◽

Data Driven ◽

Future Directions ◽

Content Type ◽

Advantages And Disadvantages ◽

Analysis Process ◽

Data Driven Approach

Purpose This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain. Design/methodology/approach This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis. Findings This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area. Originality/value To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants.

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Data science for engineering design: State of the art and future directions

Computers in Industry ◽

10.1016/j.compind.2021.103447 ◽

2021 ◽

Vol 129 ◽

pp. 103447

Author(s):

Filippo Chiarello ◽

Paola Belingheri ◽

Gualtiero Fantoni

Keyword(s):

Engineering Design ◽

Data Science ◽

State Of The Art ◽

Future Directions

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Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000240 ◽

2016 ◽

Vol 224 (2) ◽

pp. 62-70 ◽

Cited By ~ 2

Author(s):

Thomas Straube

Keyword(s):

Anxiety Disorders ◽

Neuronal Plasticity ◽

Functional Neuroimaging ◽

State Of The Art ◽

Methodological Issues ◽

Future Directions ◽

Activation Patterns ◽

New Learning ◽

Analytical Strategies ◽

Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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